Method

Multi-stage Association based on sensor fusion for 3d multi-object tracking [la][on] [MSA-MOT]


Submitted on 14 Aug. 2022 08:12 by
zhu ziming (HANGZHOU DIANZI UNIVERSITY)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
To achieve accurate and fast
tracking, we propose a multi-stage association
framework for 3D-MOT.
Parameters:
TBD
Latex Bibtex:
@Article{s22228650,
AUTHOR = {Zhu, Ziming and Nie, Jiahao and Wu, Han
and He, Zhiwei and Gao, Mingyu},
TITLE = {MSA-MOT: Multi-Stage Association for 3D
Multimodality Multi-Object Tracking},
JOURNAL = {Sensors},
VOLUME = {22},
YEAR = {2022},
NUMBER = {22},
ARTICLE-NUMBER = {8650},
URL = {https://www.mdpi.com/1424-8220/22/22/8650},
PubMedID = {36433246},
ISSN = {1424-8220},
DOI = {10.3390/s22228650}
}

Detailed Results

From all 29 test sequences, our benchmark computes the commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 88.19 % 85.47 % 88.35 % 88.24 %
PEDESTRIAN 47.84 % 64.64 % 48.90 % 88.60 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 94.75 % 94.53 % 94.64 % 35360 2046 1960 18.39 % 45087 1781
PEDESTRIAN 66.85 % 79.18 % 72.49 % 15588 4100 7730 36.86 % 23764 1558

Benchmark MT PT ML IDS FRAG
CAR 87.23 % 11.54 % 1.23 % 56 405
PEDESTRIAN 33.33 % 50.52 % 16.15 % 244 1393

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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